






import math
import sys





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	""""""
	try:
		with open(a_					,	"""rb"""       ) as binary_file:
			__lowercase   							=	binary_file.read()
		for dat in data:
			__lowercase   							=	F'{dat:08b}'
			result += curr_byte
		return result
	except OSError:
		print("""File not accessible"""       )
		sys.exit()





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	{"""0""": """0""", """1""": """1"""}
	__lowercase							,    __lowercase   							=	"""""", """"""
	__lowercase   							=	len(a_       )

	for i in range(len(a_       )       ):
		curr_string += data_bits[i]
		if curr_string not in lexicon:
			continue

		__lowercase   							=	lexicon[curr_string]
		result += last_match_id
		__lowercase   							=	last_match_id + """0"""

		if math.loga(a_       ).is_integer():
			__lowercase   							=	{}
			for curr_key in list(a_       ):
				__lowercase   							=	lexicon.pop(a_       )
			__lowercase   							=	new_lex

		__lowercase   							=	last_match_id + """1"""
		index += 1
		__lowercase   							=	""""""
	return result





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	8
	try:
		with open(a_					,	"""wb"""       ) as opened_file:
			__lowercase   							=	[
			    to_write[i : i + byte_length]
			    for i in range(0					,	len(a_       )					,	a_       )
			]

			if len(result_byte_array[-1]       ) % byte_length == 0:
				result_byte_array.append("""10000000"""       )
			else:
				result_byte_array[-1] += "1" + "0" * (
				    byte_length - len(result_byte_array[-1]       ) - 1
				)

			for elem in result_byte_array[:-1]:
				opened_file.write(int(a_					,	2       ).to_bytes(1					,	byteorder="""big"""       )       )
	except OSError:
		print("""File not accessible"""       )
		sys.exit()





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	0
	for letter in data_bits:
		if letter == "1":
			break
		counter += 1

	__lowercase   							=	data_bits[counter:]
	__lowercase   							=	data_bits[counter + 1 :]
	return data_bits





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	__lowercase   							=	read_file_binary(a_       )
	__lowercase   							=	remove_prefix(a_       )
	__lowercase   							=	decompress_data(a_       )
	write_file_binary(a_					,	a_       )


if __name__ == "__main__":
						compress(sys.argv[1], sys.argv[2])







'''simple docstring'''


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'					,       [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Dict      ):
  __a								=       tmp_path_factory.mktemp('dset_infos_dir'      )
  if "full:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write('---\ndataset_info:\n  dataset_size: 42\n---'      )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / 'README.md'					,       'w'      ) as f:
      f.write(''      )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / 'dataset_infos.json'					,       'w'      ) as f:
      f.write('{"default": {"dataset_size": 42}}'      )
  __a								=       DatasetInfosDict.from_directory(a_      )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'					,       [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				DatasetInfo      ):
  __a								=       str(a_      )
  dataset_info.write_to_directory(a_      )
  __a								=       DatasetInfo.from_directory(a_      )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(a_					,       'dataset_info.json'      )      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo(
      description='foo'					,       citation='bar'					,       homepage='https://foo.bar'					,       license='CC0'					,       features=Features({'a': Value('int32'      )}      )					,       post_processed={}					,       supervised_keys=()					,       task_templates=[]					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train', 'num_examples': 42}]					,       download_checksums={}					,       download_size=1337					,       post_processing_size=442					,       dataset_size=1234					,       size_in_bytes=1337 + 442 + 1234					,       )
  __a								=       dataset_info._to_yaml_dict()
  assert sorted(a_      ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML      )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]					,       (list, dict, int, str)      )
  __a								=       yaml.safe_dump(a_      )
  __a								=       yaml.safe_load(a_      )
  assert dataset_info_yaml_dict == reloaded


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       DatasetInfo()
  __a								=       dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    'dataset_infos_dict'					,       [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}      ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}      ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,       features=Features({'a': Value('int32'      )}      )					,       builder_name='builder'					,       config_name='config'					,       version='1.0.0'					,       splits=[{'name': 'train'}]					,       download_size=42					,       )
            }      ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42      ),
                'v2': DatasetInfo(dataset_size=1337      ),
            }      ),
    ]					,       )
def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				DatasetInfosDict      ):
  __a								=       str(a_      )
  dataset_infos_dict.write_to_directory(a_      )
  __a								=       DatasetInfosDict.from_directory(a_      )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    __a								=       config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    __a								=       DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()      )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(a_					,       'README.md'      )      )



